The exponential growth of sequence databases in the genomics era was promoted by the desire to understand fundamental macromolecular structural mechanisms, and how these structures interact with oligonucleotides, proteins and metabolites in a cell. However, a huge discrepancy in the number of corresponding 3D structures available exists. Current experimental methods of 3D structure determination, such as X-ray crystallography and Nuclear Magnetic Resonance, can be cumbersome and problematic, requiring a huge time investment as well as a team of experts that are trained in these techniques. To address these concerns software tools were developed to model macromolecular complexes, but they are rudimentary in that they are still very computationally expensive, they cannot model very large macromolecular structures such as the ribosome, they are not unified into a single platform, and they have not been critically evaluated to date. In this Fast Track SBIR application DNA Software, Inc. proposes to extend the functionality of an existing 3D homology structure prediction platform. Our current prototype software, RNA-123, has focused upon RNA and it can already accurately predict the 3D structures of molecules the size of 5S rRNA (~120 nucleotides long) using sequence-based homology modeling. The engineering and extension of the functionality of this software will enable a researcher, for the very first time, to study the structural mechanisms of a whole ribosome. RNA-123 will make it possible to model pathogenic bacterial ribosomes, where no crystal structures currently exist, to exploit them in a timely manner to develop new classes of rationally designed RNA-targeted drugs. This objective will be accomplished in seven specific aims (aims 1-3 in Phase I and aims 1-4 in Phase II): Aim 1.1: Engineer existing 3D prediction technology, RNA-123, and extend its capabilities to allow for the prediction of 3D structures of larger sequences and complexes. Aim 1.2: Predict the 3D structure of 16S rRNA of T. Thermophilus by using the known 3D crystal structure of 16S rRNA of E. coli as a template and vice versa. Aim 1.3: Test the software developed in aim 1.1 by assessing the 16S rRNA structures predicted in aim 1.2. Aim 2.1: To extend the capabilities of the software developed in phase 1 to allow homology modeling of protein-RNA complexes the size of a bacterial or eukaryotic ribosome. Aim 2.2: Predict and evaluate structures for the known complete ribosomes (70S) and ribosomal subunits (30S and 50S) to test the software developed in aims 1 of phase I and II. Aim 2.3: Correlation of predicted 3D structures of E. coli's 16S rRNA mutants with their biological activity. Aim 2.4: Predict 3D structure of the complete ribosomes of P.aeruginosa, and S. aureus, 2 clinically important pathogens. This grant will have long-term effects on the scientific community as a whole, because once the functionality of RNA-123 is extended to be able to homology model an entire ribosome, it can be easily adapted to model biopolymers, DNA, and carbohydrates as well as their complexes with each other, RNA, and drug-like small molecules. RNA-123 will enable scientists with the latest, most accurate bioinformatics tool available, having an immediate impact on all of humanity.